On the Design of Clean-Slate Network Control and Management Plane

We provide a design of clean-slate control and management plane for data networks using the abstraction of 4D architecture, utilizing and extending 4D’s concept of a logically centralized Decision plane that is responsible for managing network-wide resources. In this paper, a scalable protocol and a dynamically adaptable algorithm for assigning Data plane devices to a physically distributed Decision plane are investigated, that enable a network to operate with minimal configuration and human intervention while providing optimal convergence and robustness against failures. Our work is especially relevant in the context of ISPs and large geographically dispersed enterprise networks. We also provide an extensive evaluation of our algorithm using real-world and artificially generated ISP topologies along with an experimental evaluation using ns-2 simulator

Using artificial intelligence in routing schemes for wireless networks

For the latest 10 years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore the performance of two very well-known routing paradigms, directed diffusion and Energy-Aware Routing, and our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

Energy management in communication networks: a journey through modelling and optimization glasses

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed

Exploring the benefit of rerouting multi-period traffic to multi-site data centers

In cloud-like scenarios, demand is served at one of multiple possible data center (DC) destinations. Usually, the exact DC that is used can be freely chosen, which leads to an anycast routing problem. Furthermore, the demand volume is expected to change over time, e.g., following a diurnal pattern. Given that virtually all application domains today rely heavily on cloud-like services, it is important that the backbone networks connecting users to the DCs are resilient against failures. In this paper, we consider the problem of resiliently routing multi-period traffic: we need to find routes to both a primary DC and a backup DC (to be used in the case of failure of the primary one, or of the network connection to it), and also account for synchronization traffic between the primary and backup DCs. We formulate this as an optimization problem and adopt column generation, using a path formulation in two sub-problems: the (restricted) master problem selects "configurations" to use for each demand in each of the time epochs it lasts, while the pricing problem (PP) constructs a new "configuration" that can lead to lower overall costs (which we express as the number of network resources, i.e., bandwidth, required to serve the demand). Here, a "configuration" is defined by the network paths followed from the demand source to each of the two selected DCs, as well as that of the synchronization traffic in between the DCs. Our decomposition allows for PPs to be solved in parallel, for which we quantitatively explore the reduction in the time required to solve the overall routing problem. The key question that we address with our model is an exploration of the potential benefits of rerouting traffic from one time epoch to the next: we compare several (re) routing strategies, allowing traffic that spans multiple time periods to i) not be rerouted in different periods, ii) only change the backup DC and routes, or iii) freely change both primary and backup DC choices and the routes toward them